Overview

Dataset statistics

Number of variables24
Number of observations195
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory36.7 KiB
Average record size in memory192.7 B

Variable types

Categorical2
Numeric22

Alerts

name has a high cardinality: 195 distinct valuesHigh cardinality
MDVP:Fo(Hz) is highly overall correlated with MDVP:Fhi(Hz) and 2 other fieldsHigh correlation
MDVP:Fhi(Hz) is highly overall correlated with MDVP:Fo(Hz)High correlation
MDVP:Flo(Hz) is highly overall correlated with statusHigh correlation
MDVP:Jitter(%) is highly overall correlated with MDVP:Jitter(Abs) and 13 other fieldsHigh correlation
MDVP:Jitter(Abs) is highly overall correlated with MDVP:Fo(Hz) and 15 other fieldsHigh correlation
MDVP:RAP is highly overall correlated with MDVP:Jitter(%) and 13 other fieldsHigh correlation
MDVP:PPQ is highly overall correlated with MDVP:Jitter(%) and 13 other fieldsHigh correlation
Jitter:DDP is highly overall correlated with MDVP:Jitter(%) and 13 other fieldsHigh correlation
MDVP:Shimmer is highly overall correlated with MDVP:Jitter(%) and 14 other fieldsHigh correlation
MDVP:Shimmer(dB) is highly overall correlated with MDVP:Jitter(%) and 14 other fieldsHigh correlation
Shimmer:APQ3 is highly overall correlated with MDVP:Jitter(%) and 14 other fieldsHigh correlation
Shimmer:APQ5 is highly overall correlated with MDVP:Jitter(%) and 14 other fieldsHigh correlation
MDVP:APQ is highly overall correlated with MDVP:Jitter(%) and 15 other fieldsHigh correlation
Shimmer:DDA is highly overall correlated with MDVP:Jitter(%) and 14 other fieldsHigh correlation
NHR is highly overall correlated with MDVP:Jitter(%) and 15 other fieldsHigh correlation
HNR is highly overall correlated with MDVP:Jitter(%) and 14 other fieldsHigh correlation
RPDE is highly overall correlated with MDVP:Jitter(Abs) and 10 other fieldsHigh correlation
spread1 is highly overall correlated with MDVP:Jitter(%) and 16 other fieldsHigh correlation
spread2 is highly overall correlated with MDVP:APQ and 2 other fieldsHigh correlation
D2 is highly overall correlated with NHRHigh correlation
PPE is highly overall correlated with MDVP:Jitter(%) and 16 other fieldsHigh correlation
status is highly overall correlated with MDVP:Fo(Hz) and 3 other fieldsHigh correlation
name is uniformly distributedUniform
name has unique valuesUnique
MDVP:Fo(Hz) has unique valuesUnique
MDVP:Fhi(Hz) has unique valuesUnique
MDVP:Flo(Hz) has unique valuesUnique
HNR has unique valuesUnique
RPDE has unique valuesUnique
DFA has unique valuesUnique
spread1 has unique valuesUnique
D2 has unique valuesUnique
PPE has unique valuesUnique

Reproduction

Analysis started2023-04-27 08:17:51.484702
Analysis finished2023-04-27 08:20:01.055982
Duration2 minutes and 9.57 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

name
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct195
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
phon_R01_S01_1
 
1
phon_R01_S35_1
 
1
phon_R01_S31_3
 
1
phon_R01_S31_4
 
1
phon_R01_S31_5
 
1
Other values (190)
190 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters2730
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique195 ?
Unique (%)100.0%

Sample

1st rowphon_R01_S01_1
2nd rowphon_R01_S01_2
3rd rowphon_R01_S01_3
4th rowphon_R01_S01_4
5th rowphon_R01_S01_5

Common Values

ValueCountFrequency (%)
phon_R01_S01_1 1
 
0.5%
phon_R01_S35_1 1
 
0.5%
phon_R01_S31_3 1
 
0.5%
phon_R01_S31_4 1
 
0.5%
phon_R01_S31_5 1
 
0.5%
phon_R01_S31_6 1
 
0.5%
phon_R01_S32_1 1
 
0.5%
phon_R01_S32_2 1
 
0.5%
phon_R01_S32_3 1
 
0.5%
phon_R01_S32_4 1
 
0.5%
Other values (185) 185
94.9%

Length

2023-04-27T08:20:01.192457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
phon_r01_s01_1 1
 
0.5%
phon_r01_s13_2 1
 
0.5%
phon_r01_s02_6 1
 
0.5%
phon_r01_s01_3 1
 
0.5%
phon_r01_s01_4 1
 
0.5%
phon_r01_s01_5 1
 
0.5%
phon_r01_s01_6 1
 
0.5%
phon_r01_s02_1 1
 
0.5%
phon_r01_s02_2 1
 
0.5%
phon_r01_s02_3 1
 
0.5%
Other values (185) 185
94.9%

Most occurring characters

ValueCountFrequency (%)
_ 585
21.4%
1 282
10.3%
0 255
9.3%
p 195
 
7.1%
h 195
 
7.1%
S 195
 
7.1%
R 195
 
7.1%
n 195
 
7.1%
o 195
 
7.1%
2 100
 
3.7%
Other values (7) 338
12.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 975
35.7%
Lowercase Letter 780
28.6%
Connector Punctuation 585
21.4%
Uppercase Letter 390
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 282
28.9%
0 255
26.2%
2 100
 
10.3%
3 93
 
9.5%
4 80
 
8.2%
5 57
 
5.8%
6 50
 
5.1%
7 28
 
2.9%
9 18
 
1.8%
8 12
 
1.2%
Lowercase Letter
ValueCountFrequency (%)
p 195
25.0%
h 195
25.0%
n 195
25.0%
o 195
25.0%
Uppercase Letter
ValueCountFrequency (%)
S 195
50.0%
R 195
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 585
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1560
57.1%
Latin 1170
42.9%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 585
37.5%
1 282
18.1%
0 255
16.3%
2 100
 
6.4%
3 93
 
6.0%
4 80
 
5.1%
5 57
 
3.7%
6 50
 
3.2%
7 28
 
1.8%
9 18
 
1.2%
Latin
ValueCountFrequency (%)
p 195
16.7%
h 195
16.7%
S 195
16.7%
R 195
16.7%
n 195
16.7%
o 195
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2730
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 585
21.4%
1 282
10.3%
0 255
9.3%
p 195
 
7.1%
h 195
 
7.1%
S 195
 
7.1%
R 195
 
7.1%
n 195
 
7.1%
o 195
 
7.1%
2 100
 
3.7%
Other values (7) 338
12.4%

MDVP:Fo(Hz)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct195
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154.22864
Minimum88.333
Maximum260.105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-27T08:20:01.424171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum88.333
5-th percentile101.8791
Q1117.572
median148.79
Q3182.769
95-th percentile236.5078
Maximum260.105
Range171.772
Interquartile range (IQR)65.197

Descriptive statistics

Standard deviation41.390065
Coefficient of variation (CV)0.26836821
Kurtosis-0.62789811
Mean154.22864
Median Absolute Deviation (MAD)31.786
Skewness0.59173746
Sum30074.585
Variance1713.1375
MonotonicityNot monotonic
2023-04-27T08:20:01.694941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
119.992 1
 
0.5%
169.774 1
 
0.5%
156.239 1
 
0.5%
145.174 1
 
0.5%
138.145 1
 
0.5%
166.888 1
 
0.5%
119.031 1
 
0.5%
120.078 1
 
0.5%
120.289 1
 
0.5%
120.256 1
 
0.5%
Other values (185) 185
94.9%
ValueCountFrequency (%)
88.333 1
0.5%
91.904 1
0.5%
95.056 1
0.5%
95.385 1
0.5%
95.605 1
0.5%
95.73 1
0.5%
96.106 1
0.5%
98.804 1
0.5%
100.77 1
0.5%
100.96 1
0.5%
ValueCountFrequency (%)
260.105 1
0.5%
252.455 1
0.5%
245.51 1
0.5%
244.99 1
0.5%
243.439 1
0.5%
242.852 1
0.5%
241.404 1
0.5%
240.301 1
0.5%
237.323 1
0.5%
237.226 1
0.5%

MDVP:Fhi(Hz)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct195
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean197.10492
Minimum102.145
Maximum592.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-27T08:20:01.959341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum102.145
5-th percentile115.8188
Q1134.8625
median175.829
Q3224.2055
95-th percentile410.6398
Maximum592.03
Range489.885
Interquartile range (IQR)89.343

Descriptive statistics

Standard deviation91.491548
Coefficient of variation (CV)0.46417689
Kurtosis7.6272412
Mean197.10492
Median Absolute Deviation (MAD)42.485
Skewness2.542146
Sum38435.459
Variance8370.7033
MonotonicityNot monotonic
2023-04-27T08:20:02.243255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157.302 1
 
0.5%
191.759 1
 
0.5%
195.107 1
 
0.5%
198.109 1
 
0.5%
197.238 1
 
0.5%
198.966 1
 
0.5%
127.533 1
 
0.5%
126.632 1
 
0.5%
128.143 1
 
0.5%
125.306 1
 
0.5%
Other values (185) 185
94.9%
ValueCountFrequency (%)
102.145 1
0.5%
102.305 1
0.5%
107.715 1
0.5%
108.664 1
0.5%
110.019 1
0.5%
112.24 1
0.5%
112.777 1
0.5%
113.597 1
0.5%
113.84 1
0.5%
115.697 1
0.5%
ValueCountFrequency (%)
592.03 1
0.5%
588.518 1
0.5%
586.567 1
0.5%
581.289 1
0.5%
565.74 1
0.5%
492.892 1
0.5%
479.697 1
0.5%
450.247 1
0.5%
442.824 1
0.5%
442.557 1
0.5%

MDVP:Flo(Hz)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct195
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.32463
Minimum65.476
Maximum239.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-27T08:20:02.499065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum65.476
5-th percentile68.9464
Q184.291
median104.315
Q3140.0185
95-th percentile220.1949
Maximum239.17
Range173.694
Interquartile range (IQR)55.7275

Descriptive statistics

Standard deviation43.521413
Coefficient of variation (CV)0.37413756
Kurtosis0.65461452
Mean116.32463
Median Absolute Deviation (MAD)23.678
Skewness1.2173504
Sum22683.303
Variance1894.1134
MonotonicityNot monotonic
2023-04-27T08:20:02.772493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74.997 1
 
0.5%
151.451 1
 
0.5%
79.82 1
 
0.5%
80.637 1
 
0.5%
81.114 1
 
0.5%
79.512 1
 
0.5%
109.216 1
 
0.5%
105.667 1
 
0.5%
100.209 1
 
0.5%
104.773 1
 
0.5%
Other values (185) 185
94.9%
ValueCountFrequency (%)
65.476 1
0.5%
65.75 1
0.5%
65.782 1
0.5%
65.809 1
0.5%
66.004 1
0.5%
66.157 1
0.5%
67.021 1
0.5%
67.343 1
0.5%
68.401 1
0.5%
68.623 1
0.5%
ValueCountFrequency (%)
239.17 1
0.5%
237.303 1
0.5%
232.483 1
0.5%
232.435 1
0.5%
231.848 1
0.5%
229.256 1
0.5%
227.911 1
0.5%
225.227 1
0.5%
223.634 1
0.5%
221.156 1
0.5%

MDVP:Jitter(%)
Real number (ℝ)

Distinct173
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0062204615
Minimum0.00168
Maximum0.03316
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-27T08:20:03.041144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.00168
5-th percentile0.002211
Q10.00346
median0.00494
Q30.007365
95-th percentile0.015561
Maximum0.03316
Range0.03148
Interquartile range (IQR)0.003905

Descriptive statistics

Standard deviation0.0048481337
Coefficient of variation (CV)0.77938488
Kurtosis12.030939
Mean0.0062204615
Median Absolute Deviation (MAD)0.0018
Skewness3.0849462
Sum1.21299
Variance2.35044 × 10-5
MonotonicityNot monotonic
2023-04-27T08:20:03.293853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00694 3
 
1.5%
0.00742 3
 
1.5%
0.00369 3
 
1.5%
0.00784 2
 
1.0%
0.00314 2
 
1.0%
0.00448 2
 
1.0%
0.00451 2
 
1.0%
0.00346 2
 
1.0%
0.00258 2
 
1.0%
0.00298 2
 
1.0%
Other values (163) 172
88.2%
ValueCountFrequency (%)
0.00168 1
0.5%
0.00174 1
0.5%
0.00178 1
0.5%
0.0018 1
0.5%
0.00183 1
0.5%
0.00185 1
0.5%
0.00198 1
0.5%
0.00205 1
0.5%
0.0021 1
0.5%
0.00212 1
0.5%
ValueCountFrequency (%)
0.03316 1
0.5%
0.03107 1
0.5%
0.03011 1
0.5%
0.02714 1
0.5%
0.01936 1
0.5%
0.01872 1
0.5%
0.01813 1
0.5%
0.01719 1
0.5%
0.01627 1
0.5%
0.01568 1
0.5%

MDVP:Jitter(Abs)
Real number (ℝ)

Distinct19
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3958974 × 10-5
Minimum7 × 10-6
Maximum0.00026
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-27T08:20:03.534283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum7 × 10-6
5-th percentile1 × 10-5
Q12 × 10-5
median3 × 10-5
Q36 × 10-5
95-th percentile0.0001
Maximum0.00026
Range0.000253
Interquartile range (IQR)4 × 10-5

Descriptive statistics

Standard deviation3.4821909 × 10-5
Coefficient of variation (CV)0.79214561
Kurtosis10.869043
Mean4.3958974 × 10-5
Median Absolute Deviation (MAD)1 × 10-5
Skewness2.6490714
Sum0.008572
Variance1.2125653 × 10-9
MonotonicityNot monotonic
2023-04-27T08:20:03.790607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 × 10-546
23.6%
4 × 10-528
14.4%
2 × 10-528
14.4%
1 × 10-520
10.3%
5 × 10-517
 
8.7%
6 × 10-516
 
8.2%
8 × 10-59
 
4.6%
7 × 10-58
 
4.1%
9 × 10-55
 
2.6%
9 × 10-65
 
2.6%
Other values (9) 13
 
6.7%
ValueCountFrequency (%)
7 × 10-61
 
0.5%
9 × 10-65
 
2.6%
1 × 10-520
10.3%
2 × 10-528
14.4%
3 × 10-546
23.6%
4 × 10-528
14.4%
5 × 10-517
 
8.7%
6 × 10-516
 
8.2%
7 × 10-58
 
4.1%
8 × 10-59
 
4.6%
ValueCountFrequency (%)
0.00026 1
 
0.5%
0.00022 1
 
0.5%
0.00016 1
 
0.5%
0.00015 2
 
1.0%
0.00014 1
 
0.5%
0.00012 1
 
0.5%
0.00011 2
 
1.0%
0.0001 3
 
1.5%
9 × 10-55
2.6%
8 × 10-59
4.6%

MDVP:RAP
Real number (ℝ)

Distinct155
Distinct (%)79.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0033064103
Minimum0.00068
Maximum0.02144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-27T08:20:04.044338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.00068
5-th percentile0.001118
Q10.00166
median0.0025
Q30.003835
95-th percentile0.008756
Maximum0.02144
Range0.02076
Interquartile range (IQR)0.002175

Descriptive statistics

Standard deviation0.0029677744
Coefficient of variation (CV)0.89758203
Kurtosis14.213798
Mean0.0033064103
Median Absolute Deviation (MAD)0.00098
Skewness3.3607085
Sum0.64475
Variance8.807685 × 10-6
MonotonicityNot monotonic
2023-04-27T08:20:04.311538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00169 5
 
2.6%
0.00134 3
 
1.5%
0.00428 3
 
1.5%
0.0025 3
 
1.5%
0.00157 3
 
1.5%
0.00165 3
 
1.5%
0.0037 2
 
1.0%
0.00244 2
 
1.0%
0.00316 2
 
1.0%
0.00331 2
 
1.0%
Other values (145) 167
85.6%
ValueCountFrequency (%)
0.00068 1
0.5%
0.00075 1
0.5%
0.00076 1
0.5%
0.00092 1
0.5%
0.00093 1
0.5%
0.00094 1
0.5%
0.001 1
0.5%
0.00105 2
1.0%
0.00109 1
0.5%
0.00113 1
0.5%
ValueCountFrequency (%)
0.02144 1
0.5%
0.01854 1
0.5%
0.018 1
0.5%
0.01568 1
0.5%
0.01159 1
0.5%
0.01117 1
0.5%
0.01075 1
0.5%
0.00996 1
0.5%
0.00919 1
0.5%
0.00905 1
0.5%

MDVP:PPQ
Real number (ℝ)

Distinct165
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.003446359
Minimum0.00092
Maximum0.01958
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-27T08:20:04.578578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.00092
5-th percentile0.001315
Q10.00186
median0.00269
Q30.003955
95-th percentile0.009083
Maximum0.01958
Range0.01866
Interquartile range (IQR)0.002095

Descriptive statistics

Standard deviation0.0027589766
Coefficient of variation (CV)0.80054825
Kurtosis11.963922
Mean0.003446359
Median Absolute Deviation (MAD)0.00094
Skewness3.0738925
Sum0.67204
Variance7.6119521 × 10-6
MonotonicityNot monotonic
2023-04-27T08:20:04.929510image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00332 4
 
2.1%
0.00283 3
 
1.5%
0.00203 3
 
1.5%
0.00182 3
 
1.5%
0.00113 2
 
1.0%
0.00194 2
 
1.0%
0.00192 2
 
1.0%
0.00312 2
 
1.0%
0.00258 2
 
1.0%
0.0039 2
 
1.0%
Other values (155) 170
87.2%
ValueCountFrequency (%)
0.00092 1
0.5%
0.00096 1
0.5%
0.001 1
0.5%
0.00106 1
0.5%
0.00107 1
0.5%
0.00113 2
1.0%
0.00115 1
0.5%
0.00122 1
0.5%
0.00128 1
0.5%
0.00133 1
0.5%
ValueCountFrequency (%)
0.01958 1
0.5%
0.01699 1
0.5%
0.01628 1
0.5%
0.01522 1
0.5%
0.01154 1
0.5%
0.01027 1
0.5%
0.0099 1
0.5%
0.00963 1
0.5%
0.00946 1
0.5%
0.00909 1
0.5%

Jitter:DDP
Real number (ℝ)

Distinct180
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0099199487
Minimum0.00204
Maximum0.06433
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-27T08:20:05.421549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.00204
5-th percentile0.003354
Q10.004985
median0.00749
Q30.011505
95-th percentile0.026271
Maximum0.06433
Range0.06229
Interquartile range (IQR)0.00652

Descriptive statistics

Standard deviation0.0089033444
Coefficient of variation (CV)0.89751919
Kurtosis14.224762
Mean0.0099199487
Median Absolute Deviation (MAD)0.00293
Skewness3.3620584
Sum1.93439
Variance7.9269541 × 10-5
MonotonicityNot monotonic
2023-04-27T08:20:05.919574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00507 3
 
1.5%
0.01109 2
 
1.0%
0.0078 2
 
1.0%
0.0075 2
 
1.0%
0.00731 2
 
1.0%
0.00994 2
 
1.0%
0.00696 2
 
1.0%
0.01285 2
 
1.0%
0.00616 2
 
1.0%
0.00496 2
 
1.0%
Other values (170) 174
89.2%
ValueCountFrequency (%)
0.00204 1
0.5%
0.00225 1
0.5%
0.00229 1
0.5%
0.00276 1
0.5%
0.00278 1
0.5%
0.00283 1
0.5%
0.00301 1
0.5%
0.00314 1
0.5%
0.00315 1
0.5%
0.00327 1
0.5%
ValueCountFrequency (%)
0.06433 1
0.5%
0.05563 1
0.5%
0.05401 1
0.5%
0.04705 1
0.5%
0.03476 1
0.5%
0.03351 1
0.5%
0.03225 1
0.5%
0.02987 1
0.5%
0.02756 1
0.5%
0.02716 1
0.5%

MDVP:Shimmer
Real number (ℝ)

Distinct188
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.029709128
Minimum0.00954
Maximum0.11908
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-27T08:20:06.418281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.00954
5-th percentile0.011211
Q10.016505
median0.02297
Q30.037885
95-th percentile0.067256
Maximum0.11908
Range0.10954
Interquartile range (IQR)0.02138

Descriptive statistics

Standard deviation0.018856932
Coefficient of variation (CV)0.63471845
Kurtosis3.2383081
Mean0.029709128
Median Absolute Deviation (MAD)0.00839
Skewness1.6664804
Sum5.79328
Variance0.00035558388
MonotonicityNot monotonic
2023-04-27T08:20:06.858156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02448 2
 
1.0%
0.03273 2
 
1.0%
0.01608 2
 
1.0%
0.01725 2
 
1.0%
0.02293 2
 
1.0%
0.0145 2
 
1.0%
0.01503 2
 
1.0%
0.01412 1
 
0.5%
0.04479 1
 
0.5%
0.02503 1
 
0.5%
Other values (178) 178
91.3%
ValueCountFrequency (%)
0.00954 1
0.5%
0.00958 1
0.5%
0.01015 1
0.5%
0.01022 1
0.5%
0.01024 1
0.5%
0.0103 1
0.5%
0.01033 1
0.5%
0.01043 1
0.5%
0.01064 1
0.5%
0.01098 1
0.5%
ValueCountFrequency (%)
0.11908 1
0.5%
0.09419 1
0.5%
0.09178 1
0.5%
0.08684 1
0.5%
0.08143 1
0.5%
0.07959 1
0.5%
0.0717 1
0.5%
0.07118 1
0.5%
0.06734 1
0.5%
0.06727 1
0.5%

MDVP:Shimmer(dB)
Real number (ℝ)

Distinct149
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28225128
Minimum0.085
Maximum1.302
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-27T08:20:07.330922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.085
5-th percentile0.1018
Q10.1485
median0.221
Q30.35
95-th percentile0.6527
Maximum1.302
Range1.217
Interquartile range (IQR)0.2015

Descriptive statistics

Standard deviation0.19487729
Coefficient of variation (CV)0.69043899
Kurtosis5.1281925
Mean0.28225128
Median Absolute Deviation (MAD)0.086
Skewness1.9993886
Sum55.039
Variance0.037977158
MonotonicityNot monotonic
2023-04-27T08:20:07.781923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.154 5
 
2.6%
0.197 4
 
2.1%
0.155 3
 
1.5%
0.126 3
 
1.5%
0.129 3
 
1.5%
0.145 3
 
1.5%
0.307 3
 
1.5%
0.255 3
 
1.5%
0.085 2
 
1.0%
0.107 2
 
1.0%
Other values (139) 164
84.1%
ValueCountFrequency (%)
0.085 2
1.0%
0.089 1
0.5%
0.09 1
0.5%
0.093 1
0.5%
0.094 1
0.5%
0.097 2
1.0%
0.098 1
0.5%
0.099 1
0.5%
0.103 1
0.5%
0.106 2
1.0%
ValueCountFrequency (%)
1.302 1
0.5%
1.018 1
0.5%
0.93 1
0.5%
0.891 1
0.5%
0.833 1
0.5%
0.821 1
0.5%
0.784 1
0.5%
0.772 1
0.5%
0.722 1
0.5%
0.659 1
0.5%

Shimmer:APQ3
Real number (ℝ)

Distinct184
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.015664154
Minimum0.00455
Maximum0.05647
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-27T08:20:08.203089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.00455
5-th percentile0.005368
Q10.008245
median0.01279
Q30.020265
95-th percentile0.036227
Maximum0.05647
Range0.05192
Interquartile range (IQR)0.01202

Descriptive statistics

Standard deviation0.010153162
Coefficient of variation (CV)0.64817811
Kurtosis2.7201516
Mean0.015664154
Median Absolute Deviation (MAD)0.0051
Skewness1.5805764
Sum3.05451
Variance0.00010308669
MonotonicityNot monotonic
2023-04-27T08:20:08.726805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01441 2
 
1.0%
0.00829 2
 
1.0%
0.01277 2
 
1.0%
0.01484 2
 
1.0%
0.00728 2
 
1.0%
0.00469 2
 
1.0%
0.01579 2
 
1.0%
0.00633 2
 
1.0%
0.01284 2
 
1.0%
0.00522 2
 
1.0%
Other values (174) 175
89.7%
ValueCountFrequency (%)
0.00455 1
0.5%
0.00468 1
0.5%
0.00469 2
1.0%
0.00476 1
0.5%
0.0049 1
0.5%
0.00504 1
0.5%
0.00522 2
1.0%
0.00534 1
0.5%
0.00538 1
0.5%
0.00557 1
0.5%
ValueCountFrequency (%)
0.05647 1
0.5%
0.05551 1
0.5%
0.05358 1
0.5%
0.04421 1
0.5%
0.04284 1
0.5%
0.04016 1
0.5%
0.03804 1
0.5%
0.03788 1
0.5%
0.03671 1
0.5%
0.0365 1
0.5%

Shimmer:APQ5
Real number (ℝ)

Distinct189
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.017878256
Minimum0.0057
Maximum0.0794
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-27T08:20:09.200841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.0057
5-th percentile0.006383
Q10.00958
median0.01347
Q30.02238
95-th percentile0.042701
Maximum0.0794
Range0.0737
Interquartile range (IQR)0.0128

Descriptive statistics

Standard deviation0.012023706
Coefficient of variation (CV)0.67253234
Kurtosis3.8742097
Mean0.017878256
Median Absolute Deviation (MAD)0.00468
Skewness1.7986971
Sum3.48626
Variance0.00014456949
MonotonicityNot monotonic
2023-04-27T08:20:09.719220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01219 2
 
1.0%
0.01024 2
 
1.0%
0.00747 2
 
1.0%
0.00972 2
 
1.0%
0.0116 2
 
1.0%
0.01144 2
 
1.0%
0.00621 1
 
0.5%
0.01805 1
 
0.5%
0.01859 1
 
0.5%
0.0057 1
 
0.5%
Other values (179) 179
91.8%
ValueCountFrequency (%)
0.0057 1
0.5%
0.00576 1
0.5%
0.00582 1
0.5%
0.00588 1
0.5%
0.00606 1
0.5%
0.0061 1
0.5%
0.00621 1
0.5%
0.0063 1
0.5%
0.00631 1
0.5%
0.00632 1
0.5%
ValueCountFrequency (%)
0.0794 1
0.5%
0.05556 1
0.5%
0.05426 1
0.5%
0.05005 1
0.5%
0.04962 1
0.5%
0.04825 1
0.5%
0.04791 1
0.5%
0.0458 1
0.5%
0.04518 1
0.5%
0.04282 1
0.5%

MDVP:APQ
Real number (ℝ)

Distinct189
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.024081487
Minimum0.00719
Maximum0.13778
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-27T08:20:10.047117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.00719
5-th percentile0.009114
Q10.01308
median0.01826
Q30.0294
95-th percentile0.057718
Maximum0.13778
Range0.13059
Interquartile range (IQR)0.01632

Descriptive statistics

Standard deviation0.016946736
Coefficient of variation (CV)0.70372465
Kurtosis11.163288
Mean0.024081487
Median Absolute Deviation (MAD)0.00636
Skewness2.6180465
Sum4.69589
Variance0.00028719187
MonotonicityNot monotonic
2023-04-27T08:20:10.348011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01234 2
 
1.0%
0.03772 2
 
1.0%
0.00903 2
 
1.0%
0.01491 2
 
1.0%
0.01133 2
 
1.0%
0.0114 2
 
1.0%
0.03316 1
 
0.5%
0.02259 1
 
0.5%
0.02301 1
 
0.5%
0.00811 1
 
0.5%
Other values (179) 179
91.8%
ValueCountFrequency (%)
0.00719 1
0.5%
0.00726 1
0.5%
0.00762 1
0.5%
0.00802 1
0.5%
0.00811 1
0.5%
0.0086 1
0.5%
0.00871 1
0.5%
0.00882 1
0.5%
0.00903 2
1.0%
0.00915 1
0.5%
ValueCountFrequency (%)
0.13778 1
0.5%
0.08808 1
0.5%
0.08318 1
0.5%
0.06824 1
0.5%
0.0646 1
0.5%
0.06359 1
0.5%
0.06259 1
0.5%
0.06196 1
0.5%
0.06023 1
0.5%
0.05783 1
0.5%

Shimmer:DDA
Real number (ℝ)

Distinct189
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.046992615
Minimum0.01364
Maximum0.16942
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-27T08:20:10.633530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.01364
5-th percentile0.016107
Q10.024735
median0.03836
Q30.060795
95-th percentile0.108678
Maximum0.16942
Range0.15578
Interquartile range (IQR)0.03606

Descriptive statistics

Standard deviation0.030459119
Coefficient of variation (CV)0.64816821
Kurtosis2.7206613
Mean0.046992615
Median Absolute Deviation (MAD)0.01529
Skewness1.580618
Sum9.16356
Variance0.00092775796
MonotonicityNot monotonic
2023-04-27T08:20:10.905234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02184 2
 
1.0%
0.04451 2
 
1.0%
0.01567 2
 
1.0%
0.04736 2
 
1.0%
0.01898 2
 
1.0%
0.03831 2
 
1.0%
0.01471 1
 
0.5%
0.03867 1
 
0.5%
0.03706 1
 
0.5%
0.04641 1
 
0.5%
Other values (179) 179
91.8%
ValueCountFrequency (%)
0.01364 1
0.5%
0.01403 1
0.5%
0.01406 1
0.5%
0.01407 1
0.5%
0.01428 1
0.5%
0.01471 1
0.5%
0.01513 1
0.5%
0.01567 2
1.0%
0.01603 1
0.5%
0.01614 1
0.5%
ValueCountFrequency (%)
0.16942 1
0.5%
0.16654 1
0.5%
0.16074 1
0.5%
0.13262 1
0.5%
0.12851 1
0.5%
0.12047 1
0.5%
0.11411 1
0.5%
0.11363 1
0.5%
0.11012 1
0.5%
0.10949 1
0.5%

NHR
Real number (ℝ)

Distinct185
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.024847077
Minimum0.00065
Maximum0.31482
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-27T08:20:11.190067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.00065
5-th percentile0.002528
Q10.005925
median0.01166
Q30.02564
95-th percentile0.092044
Maximum0.31482
Range0.31417
Interquartile range (IQR)0.019715

Descriptive statistics

Standard deviation0.040418449
Coefficient of variation (CV)1.6266883
Kurtosis21.994974
Mean0.024847077
Median Absolute Deviation (MAD)0.0069
Skewness4.2207091
Sum4.84518
Variance0.001633651
MonotonicityNot monotonic
2023-04-27T08:20:11.465524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.07223 2
 
1.0%
0.00476 2
 
1.0%
0.00839 2
 
1.0%
0.00231 2
 
1.0%
0.0062 2
 
1.0%
0.0042 2
 
1.0%
0.0034 2
 
1.0%
0.00681 2
 
1.0%
0.00479 2
 
1.0%
0.01049 2
 
1.0%
Other values (175) 175
89.7%
ValueCountFrequency (%)
0.00065 1
0.5%
0.00072 1
0.5%
0.00119 1
0.5%
0.00135 1
0.5%
0.00167 1
0.5%
0.00231 2
1.0%
0.00233 1
0.5%
0.00238 1
0.5%
0.00243 1
0.5%
0.00257 1
0.5%
ValueCountFrequency (%)
0.31482 1
0.5%
0.2593 1
0.5%
0.21713 1
0.5%
0.16744 1
0.5%
0.16265 1
0.5%
0.11843 1
0.5%
0.10952 1
0.5%
0.10748 1
0.5%
0.10715 1
0.5%
0.10323 1
0.5%

HNR
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct195
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.885974
Minimum8.441
Maximum33.047
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-27T08:20:11.738422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum8.441
5-th percentile13.4838
Q119.198
median22.085
Q325.0755
95-th percentile26.9742
Maximum33.047
Range24.606
Interquartile range (IQR)5.8775

Descriptive statistics

Standard deviation4.4257643
Coefficient of variation (CV)0.2022192
Kurtosis0.61603583
Mean21.885974
Median Absolute Deviation (MAD)2.945
Skewness-0.5143175
Sum4267.765
Variance19.587389
MonotonicityNot monotonic
2023-04-27T08:20:12.016243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.033 1
 
0.5%
12.359 1
 
0.5%
19.196 1
 
0.5%
18.857 1
 
0.5%
18.178 1
 
0.5%
18.33 1
 
0.5%
26.842 1
 
0.5%
26.369 1
 
0.5%
23.949 1
 
0.5%
26.017 1
 
0.5%
Other values (185) 185
94.9%
ValueCountFrequency (%)
8.441 1
0.5%
8.867 1
0.5%
9.449 1
0.5%
10.489 1
0.5%
11.744 1
0.5%
11.866 1
0.5%
12.298 1
0.5%
12.359 1
0.5%
12.435 1
0.5%
12.529 1
0.5%
ValueCountFrequency (%)
33.047 1
0.5%
32.684 1
0.5%
31.732 1
0.5%
30.94 1
0.5%
30.775 1
0.5%
29.928 1
0.5%
29.746 1
0.5%
28.409 1
0.5%
27.421 1
0.5%
27.166 1
0.5%

status
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1
147 
0
48 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters195
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 147
75.4%
0 48
 
24.6%

Length

2023-04-27T08:20:12.257645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-27T08:20:12.484760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 147
75.4%
0 48
 
24.6%

Most occurring characters

ValueCountFrequency (%)
1 147
75.4%
0 48
 
24.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 195
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 147
75.4%
0 48
 
24.6%

Most occurring scripts

ValueCountFrequency (%)
Common 195
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 147
75.4%
0 48
 
24.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 195
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 147
75.4%
0 48
 
24.6%

RPDE
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct195
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49853554
Minimum0.25657
Maximum0.685151
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-27T08:20:12.724017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.25657
5-th percentile0.3309287
Q10.421306
median0.495954
Q30.5875625
95-th percentile0.6532203
Maximum0.685151
Range0.428581
Interquartile range (IQR)0.1662565

Descriptive statistics

Standard deviation0.10394171
Coefficient of variation (CV)0.20849409
Kurtosis-0.92178098
Mean0.49853554
Median Absolute Deviation (MAD)0.082659
Skewness-0.14340241
Sum97.21443
Variance0.01080388
MonotonicityNot monotonic
2023-04-27T08:20:12.974216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.414783 1
 
0.5%
0.56161 1
 
0.5%
0.618663 1
 
0.5%
0.637518 1
 
0.5%
0.623209 1
 
0.5%
0.585169 1
 
0.5%
0.457541 1
 
0.5%
0.491345 1
 
0.5%
0.46716 1
 
0.5%
0.468621 1
 
0.5%
Other values (185) 185
94.9%
ValueCountFrequency (%)
0.25657 1
0.5%
0.263654 1
0.5%
0.27685 1
0.5%
0.296888 1
0.5%
0.305062 1
0.5%
0.305429 1
0.5%
0.306443 1
0.5%
0.311369 1
0.5%
0.32648 1
0.5%
0.329577 1
0.5%
ValueCountFrequency (%)
0.685151 1
0.5%
0.677131 1
0.5%
0.671378 1
0.5%
0.671299 1
0.5%
0.665318 1
0.5%
0.663842 1
0.5%
0.660125 1
0.5%
0.654945 1
0.5%
0.653427 1
0.5%
0.65341 1
0.5%

DFA
Real number (ℝ)

Distinct195
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.71809905
Minimum0.574282
Maximum0.825288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-27T08:20:13.253901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.574282
5-th percentile0.6323376
Q10.6747575
median0.722254
Q30.7618815
95-th percentile0.8160376
Maximum0.825288
Range0.251006
Interquartile range (IQR)0.087124

Descriptive statistics

Standard deviation0.05533583
Coefficient of variation (CV)0.077058772
Kurtosis-0.68615185
Mean0.71809905
Median Absolute Deviation (MAD)0.043369
Skewness-0.033213661
Sum140.02931
Variance0.0030620541
MonotonicityNot monotonic
2023-04-27T08:20:13.518686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.815285 1
 
0.5%
0.793509 1
 
0.5%
0.728421 1
 
0.5%
0.735546 1
 
0.5%
0.738245 1
 
0.5%
0.736964 1
 
0.5%
0.699787 1
 
0.5%
0.718839 1
 
0.5%
0.724045 1
 
0.5%
0.735136 1
 
0.5%
Other values (185) 185
94.9%
ValueCountFrequency (%)
0.574282 1
0.5%
0.58271 1
0.5%
0.605417 1
0.5%
0.623731 1
0.5%
0.62671 1
0.5%
0.627337 1
0.5%
0.628058 1
0.5%
0.628232 1
0.5%
0.630409 1
0.5%
0.631653 1
0.5%
ValueCountFrequency (%)
0.825288 1
0.5%
0.825069 1
0.5%
0.823484 1
0.5%
0.821364 1
0.5%
0.819521 1
0.5%
0.819235 1
0.5%
0.819032 1
0.5%
0.817756 1
0.5%
0.817396 1
0.5%
0.81634 1
0.5%

spread1
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct195
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.6843967
Minimum-7.964984
Maximum-2.434031
Zeros0
Zeros (%)0.0%
Negative195
Negative (%)100.0%
Memory size1.6 KiB
2023-04-27T08:20:14.945065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-7.964984
5-th percentile-7.306315
Q1-6.450096
median-5.720868
Q3-5.046192
95-th percentile-3.7336141
Maximum-2.434031
Range5.530953
Interquartile range (IQR)1.403904

Descriptive statistics

Standard deviation1.0902078
Coefficient of variation (CV)-0.19178953
Kurtosis-0.050199182
Mean-5.6843967
Median Absolute Deviation (MAD)0.71853
Skewness0.43213893
Sum-1108.4574
Variance1.188553
MonotonicityNot monotonic
2023-04-27T08:20:15.213114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4.813031 1
 
0.5%
-3.297668 1
 
0.5%
-5.944191 1
 
0.5%
-5.594275 1
 
0.5%
-5.540351 1
 
0.5%
-5.825257 1
 
0.5%
-6.890021 1
 
0.5%
-5.892061 1
 
0.5%
-6.135296 1
 
0.5%
-6.112667 1
 
0.5%
Other values (185) 185
94.9%
ValueCountFrequency (%)
-7.964984 1
0.5%
-7.777685 1
0.5%
-7.695734 1
0.5%
-7.682587 1
0.5%
-7.517934 1
0.5%
-7.496264 1
0.5%
-7.3483 1
0.5%
-7.31951 1
0.5%
-7.314237 1
0.5%
-7.31055 1
0.5%
ValueCountFrequency (%)
-2.434031 1
0.5%
-2.839756 1
0.5%
-2.929379 1
0.5%
-2.93107 1
0.5%
-3.269487 1
0.5%
-3.297668 1
0.5%
-3.377325 1
0.5%
-3.444478 1
0.5%
-3.583722 1
0.5%
-3.700544 1
0.5%

spread2
Real number (ℝ)

Distinct194
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22651035
Minimum0.006274
Maximum0.450493
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-27T08:20:15.493739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.006274
5-th percentile0.0888389
Q10.1743505
median0.218885
Q30.279234
95-th percentile0.3731391
Maximum0.450493
Range0.444219
Interquartile range (IQR)0.1048835

Descriptive statistics

Standard deviation0.083405763
Coefficient of variation (CV)0.36822054
Kurtosis-0.083022893
Mean0.22651035
Median Absolute Deviation (MAD)0.048702
Skewness0.14443049
Sum44.169518
Variance0.0069565212
MonotonicityNot monotonic
2023-04-27T08:20:15.767464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.210279 2
 
1.0%
0.266482 1
 
0.5%
0.320385 1
 
0.5%
0.12795 1
 
0.5%
0.087165 1
 
0.5%
0.115697 1
 
0.5%
0.152941 1
 
0.5%
0.195976 1
 
0.5%
0.20363 1
 
0.5%
0.217013 1
 
0.5%
Other values (184) 184
94.4%
ValueCountFrequency (%)
0.006274 1
0.5%
0.018689 1
0.5%
0.056844 1
0.5%
0.063412 1
0.5%
0.066994 1
0.5%
0.073298 1
0.5%
0.078202 1
0.5%
0.086372 1
0.5%
0.087165 1
0.5%
0.08784 1
0.5%
ValueCountFrequency (%)
0.450493 1
0.5%
0.434326 1
0.5%
0.414758 1
0.5%
0.397749 1
0.5%
0.396746 1
0.5%
0.393056 1
0.5%
0.391002 1
0.5%
0.389295 1
0.5%
0.389232 1
0.5%
0.375531 1
0.5%

D2
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct195
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3818261
Minimum1.423287
Maximum3.671155
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-27T08:20:16.037577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.423287
5-th percentile1.8487408
Q12.0991255
median2.361532
Q32.636456
95-th percentile3.0849315
Maximum3.671155
Range2.247868
Interquartile range (IQR)0.5373305

Descriptive statistics

Standard deviation0.38279905
Coefficient of variation (CV)0.16071662
Kurtosis0.2203341
Mean2.3818261
Median Absolute Deviation (MAD)0.271094
Skewness0.43038389
Sum464.45609
Variance0.14653511
MonotonicityNot monotonic
2023-04-27T08:20:16.314125image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.301442 1
 
0.5%
3.413649 1
 
0.5%
1.929715 1
 
0.5%
1.765957 1
 
0.5%
1.821297 1
 
0.5%
1.996146 1
 
0.5%
2.328513 1
 
0.5%
2.108873 1
 
0.5%
2.539724 1
 
0.5%
2.527742 1
 
0.5%
Other values (185) 185
94.9%
ValueCountFrequency (%)
1.423287 1
0.5%
1.512275 1
0.5%
1.544609 1
0.5%
1.743867 1
0.5%
1.765957 1
0.5%
1.777901 1
0.5%
1.821297 1
0.5%
1.827012 1
0.5%
1.831691 1
0.5%
1.840198 1
0.5%
ValueCountFrequency (%)
3.671155 1
0.5%
3.413649 1
0.5%
3.317586 1
0.5%
3.274865 1
0.5%
3.184027 1
0.5%
3.142364 1
0.5%
3.13655 1
0.5%
3.10901 1
0.5%
3.099301 1
0.5%
3.098256 1
0.5%

PPE
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct195
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20655164
Minimum0.044539
Maximum0.527367
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-27T08:20:16.568260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.044539
5-th percentile0.0915866
Q10.137451
median0.194052
Q30.25298
95-th percentile0.3695708
Maximum0.527367
Range0.482828
Interquartile range (IQR)0.115529

Descriptive statistics

Standard deviation0.090119322
Coefficient of variation (CV)0.43630407
Kurtosis0.52833495
Mean0.20655164
Median Absolute Deviation (MAD)0.058352
Skewness0.79749107
Sum40.27757
Variance0.0081214923
MonotonicityNot monotonic
2023-04-27T08:20:16.839226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.284654 1
 
0.5%
0.457533 1
 
0.5%
0.181988 1
 
0.5%
0.222716 1
 
0.5%
0.214075 1
 
0.5%
0.196535 1
 
0.5%
0.112856 1
 
0.5%
0.183572 1
 
0.5%
0.169923 1
 
0.5%
0.170633 1
 
0.5%
Other values (185) 185
94.9%
ValueCountFrequency (%)
0.044539 1
0.5%
0.056141 1
0.5%
0.05761 1
0.5%
0.068501 1
0.5%
0.073581 1
0.5%
0.075587 1
0.5%
0.085569 1
0.5%
0.086398 1
0.5%
0.09147 1
0.5%
0.091546 1
0.5%
ValueCountFrequency (%)
0.527367 1
0.5%
0.457533 1
0.5%
0.454721 1
0.5%
0.444774 1
0.5%
0.430788 1
0.5%
0.418646 1
0.5%
0.410335 1
0.5%
0.378483 1
0.5%
0.377429 1
0.5%
0.370961 1
0.5%

Interactions

2023-04-27T08:19:55.532962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:17:54.779826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:02.662098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:09.494120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:14.230625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:20.127125image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:26.062177image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:30.857909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:37.139134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:42.980280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:47.760787image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:54.420443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:59.457894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:04.674453image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:10.829044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:15.604592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:22.716334image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:28.170953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:32.618744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:38.760747image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:43.717206image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:49.012667image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:55.742254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:17:55.299075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:02.872948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:09.746922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:14.802110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:20.457910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:26.257964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:31.072586image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:37.541732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:43.182235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:47.978025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:54.617612image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:59.703773image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:05.028992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:11.045957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:15.831437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:23.548858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:28.394119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:32.809355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:39.138057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:43.916184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:49.250851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:55.938181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:17:56.137544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:03.074025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:09.937785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:15.004808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:20.828350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:26.865057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:31.303117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:37.925050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:43.410592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:48.190654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:54.809604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:59.941488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:05.325206image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:11.281826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:16.051881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:23.913666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:28.582706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:32.996545image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:39.495852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:44.145052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:49.466674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:56.167010image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:17:56.593130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:03.393696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:10.130154image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:15.218868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:21.186723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:27.058706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:31.526299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:38.330451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:43.638357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:48.432466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:18:55.007042image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:00.192369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:05.658954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:11.496149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-27T08:19:32.421438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:38.422265image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:43.526187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:48.820988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T08:19:55.320478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-27T08:20:17.091572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
MDVP:Fo(Hz)MDVP:Fhi(Hz)MDVP:Flo(Hz)MDVP:Jitter(%)MDVP:Jitter(Abs)MDVP:RAPMDVP:PPQJitter:DDPMDVP:ShimmerMDVP:Shimmer(dB)Shimmer:APQ3Shimmer:APQ5MDVP:APQShimmer:DDANHRHNRRPDEDFAspread1spread2D2PPEstatus
MDVP:Fo(Hz)1.0000.7960.324-0.270-0.566-0.197-0.298-0.197-0.164-0.165-0.145-0.130-0.229-0.145-0.1060.057-0.374-0.420-0.426-0.2270.252-0.4310.577
MDVP:Fhi(Hz)0.7961.0000.096-0.098-0.363-0.070-0.118-0.070-0.085-0.064-0.092-0.059-0.116-0.0920.060-0.022-0.223-0.463-0.256-0.1160.261-0.2590.393
MDVP:Flo(Hz)0.3240.0961.000-0.407-0.408-0.362-0.375-0.362-0.255-0.282-0.206-0.221-0.306-0.206-0.4580.258-0.3490.122-0.355-0.145-0.133-0.3530.523
MDVP:Jitter(%)-0.270-0.098-0.4071.0000.9190.9620.9710.9620.7290.7590.6960.6940.7310.6960.796-0.7320.4780.2090.7740.4600.3740.7710.287
MDVP:Jitter(Abs)-0.566-0.363-0.4080.9191.0000.8600.9100.8600.6640.6890.6290.6240.6910.6290.702-0.6240.5350.3220.8030.4730.2320.8000.305
MDVP:RAP-0.197-0.070-0.3620.9620.8601.0000.9661.0000.7400.7660.7160.7260.7190.7160.801-0.7540.4260.1840.7150.3460.3800.7090.245
MDVP:PPQ-0.298-0.118-0.3750.9710.9100.9661.0000.9660.7620.7860.7290.7520.7630.7290.784-0.7660.4830.2600.7940.4310.3460.8000.313
Jitter:DDP-0.197-0.070-0.3620.9620.8601.0000.9661.0000.7390.7660.7160.7250.7180.7160.801-0.7540.4250.1830.7160.3460.3800.7100.245
MDVP:Shimmer-0.164-0.085-0.2550.7290.6640.7400.7620.7391.0000.9930.9890.9870.9720.9890.770-0.8660.5380.1720.6610.4440.4220.6680.406
MDVP:Shimmer(dB)-0.165-0.064-0.2820.7590.6890.7660.7860.7660.9931.0000.9760.9760.9680.9760.798-0.8650.5200.1630.6650.4500.4340.6700.376
Shimmer:APQ3-0.145-0.092-0.2060.6960.6290.7160.7290.7160.9890.9761.0000.9780.9381.0000.728-0.8580.5110.1850.6190.4000.3890.6250.348
Shimmer:APQ5-0.130-0.059-0.2210.6940.6240.7260.7520.7250.9870.9760.9781.0000.9590.9780.740-0.8650.5100.1870.6450.4000.4100.6560.428
MDVP:APQ-0.229-0.116-0.3060.7310.6910.7190.7630.7180.9720.9680.9380.9591.0000.9380.779-0.8380.5890.1760.7130.5100.4360.7180.429
Shimmer:DDA-0.145-0.092-0.2060.6960.6290.7160.7290.7160.9890.9761.0000.9780.9381.0000.728-0.8580.5110.1850.6190.4000.3890.6250.348
NHR-0.1060.060-0.4580.7960.7020.8010.7840.8010.7700.7980.7280.7400.7790.7281.000-0.8660.619-0.1760.6590.4180.5700.6330.000
HNR0.057-0.0220.258-0.732-0.624-0.754-0.766-0.754-0.866-0.865-0.858-0.865-0.838-0.858-0.8661.000-0.6220.010-0.628-0.374-0.485-0.6380.373
RPDE-0.374-0.223-0.3490.4780.5350.4260.4830.4250.5380.5200.5110.5100.5890.5110.619-0.6221.000-0.1290.5990.4490.2010.5790.328
DFA-0.420-0.4630.1220.2090.3220.1840.2600.1830.1720.1630.1850.1870.1760.185-0.1760.010-0.1291.0000.2110.200-0.1950.2680.341
spread1-0.426-0.256-0.3550.7740.8030.7150.7940.7160.6610.6650.6190.6450.7130.6190.659-0.6280.5990.2111.0000.6500.4240.9790.599
spread2-0.227-0.116-0.1450.4600.4730.3460.4310.3460.4440.4500.4000.4000.5100.4000.418-0.3740.4490.2000.6501.0000.4840.6500.453
D20.2520.261-0.1330.3740.2320.3800.3460.3800.4220.4340.3890.4100.4360.3890.570-0.4850.201-0.1950.4240.4841.0000.4150.312
PPE-0.431-0.259-0.3530.7710.8000.7090.8000.7100.6680.6700.6250.6560.7180.6250.633-0.6380.5790.2680.9790.6500.4151.0000.624
status0.5770.3930.5230.2870.3050.2450.3130.2450.4060.3760.3480.4280.4290.3480.0000.3730.3280.3410.5990.4530.3120.6241.000

Missing values

2023-04-27T08:20:00.077290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-27T08:20:00.763184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

nameMDVP:Fo(Hz)MDVP:Fhi(Hz)MDVP:Flo(Hz)MDVP:Jitter(%)MDVP:Jitter(Abs)MDVP:RAPMDVP:PPQJitter:DDPMDVP:ShimmerMDVP:Shimmer(dB)Shimmer:APQ3Shimmer:APQ5MDVP:APQShimmer:DDANHRHNRstatusRPDEDFAspread1spread2D2PPE
0phon_R01_S01_1119.992157.30274.9970.007840.000070.003700.005540.011090.043740.4260.021820.031300.029710.065450.0221121.03310.4147830.815285-4.8130310.2664822.3014420.284654
1phon_R01_S01_2122.400148.650113.8190.009680.000080.004650.006960.013940.061340.6260.031340.045180.043680.094030.0192919.08510.4583590.819521-4.0751920.3355902.4868550.368674
2phon_R01_S01_3116.682131.111111.5550.010500.000090.005440.007810.016330.052330.4820.027570.038580.035900.082700.0130920.65110.4298950.825288-4.4431790.3111732.3422590.332634
3phon_R01_S01_4116.676137.871111.3660.009970.000090.005020.006980.015050.054920.5170.029240.040050.037720.087710.0135320.64410.4349690.819235-4.1175010.3341472.4055540.368975
4phon_R01_S01_5116.014141.781110.6550.012840.000110.006550.009080.019660.064250.5840.034900.048250.044650.104700.0176719.64910.4173560.823484-3.7477870.2345132.3321800.410335
5phon_R01_S01_6120.552131.162113.7870.009680.000080.004630.007500.013880.047010.4560.023280.035260.032430.069850.0122221.37810.4155640.825069-4.2428670.2991112.1875600.357775
6phon_R01_S02_1120.267137.244114.8200.003330.000030.001550.002020.004660.016080.1400.007790.009370.013510.023370.0060724.88610.5960400.764112-5.6343220.2576821.8547850.211756
7phon_R01_S02_2107.332113.840104.3150.002900.000030.001440.001820.004310.015670.1340.008290.009460.012560.024870.0034426.89210.6374200.763262-6.1676030.1837212.0646930.163755
8phon_R01_S02_395.730132.06891.7540.005510.000060.002930.003320.008800.020930.1910.010730.012770.017170.032180.0107021.81210.6155510.773587-5.4986780.3277692.3225110.231571
9phon_R01_S02_495.056120.10391.2260.005320.000060.002680.003320.008030.028380.2550.014410.017250.024440.043240.0102221.86210.5470370.798463-5.0118790.3259962.4327920.271362
nameMDVP:Fo(Hz)MDVP:Fhi(Hz)MDVP:Flo(Hz)MDVP:Jitter(%)MDVP:Jitter(Abs)MDVP:RAPMDVP:PPQJitter:DDPMDVP:ShimmerMDVP:Shimmer(dB)Shimmer:APQ3Shimmer:APQ5MDVP:APQShimmer:DDANHRHNRstatusRPDEDFAspread1spread2D2PPE
185phon_R01_S49_3116.286177.29196.9830.003140.000030.001340.001920.004030.015640.1360.006670.009900.016910.020010.0073724.19900.5985150.654331-5.5925840.1339172.0586580.214346
186phon_R01_S49_4116.556592.03086.2280.004960.000040.002540.002630.007620.016600.1540.008200.009720.014910.024600.0139723.95800.5664240.667654-6.4311190.1533102.1619360.120605
187phon_R01_S49_5116.342581.28994.2460.002670.000020.001150.001480.003450.013000.1170.006310.007890.011440.018920.0068025.02300.5284850.663884-6.3590180.1166362.1520830.138868
188phon_R01_S49_6114.563119.16786.6470.003270.000030.001460.001840.004390.011850.1060.005570.007210.010950.016720.0070324.77500.5553030.659132-6.7102190.1496941.9139900.121777
189phon_R01_S50_1201.774262.70778.2280.006940.000030.004120.003960.012350.025740.2550.014540.015820.017580.043630.0444119.36800.5084790.683761-6.9344740.1598902.3163460.112838
190phon_R01_S50_2174.188230.97894.2610.004590.000030.002630.002590.007900.040870.4050.023360.024980.027450.070080.0276419.51700.4484390.657899-6.5385860.1219522.6574760.133050
191phon_R01_S50_3209.516253.01789.4880.005640.000030.003310.002920.009940.027510.2630.016040.016570.018790.048120.0181019.14700.4316740.683244-6.1953250.1293032.7843120.168895
192phon_R01_S50_4174.688240.00574.2870.013600.000080.006240.005640.018730.023080.2560.012680.013650.016670.038040.1071517.88300.4075670.655683-6.7871970.1584532.6797720.131728
193phon_R01_S50_5198.764396.96174.9040.007400.000040.003700.003900.011090.022960.2410.012650.013210.015880.037940.0722319.02000.4512210.643956-6.7445770.2074542.1386080.123306
194phon_R01_S50_6214.289260.27777.9730.005670.000030.002950.003170.008850.018840.1900.010260.011610.013730.030780.0439821.20900.4628030.664357-5.7240560.1906672.5554770.148569